The document discusses various research methods used in marketing management. It covers exploratory versus conclusive research and different research designs including descriptive, causal, and case study analysis. It then discusses experiments in more detail, outlining necessary conditions for causality, ways to control extraneous variables, and how to design experiment questionnaires and manipulate variables. The document contrasts internal and external validity in experiments. It also compares lab experiments and field experiments, noting differences in factors like environment, control, demand artifacts, and costs. Finally, it briefly discusses limitations of experiments and data collection methods.

1. Marketing Management –II (MK102)
Session 7

2. Previous Session: Marketing Research Process
Source: Malhotra and Dash (6e)
Conclusive/Confirmatory
Exploratory
Research Design
Descriptive Causal
1. Pilot Surveys
2. Qualitative Research
Direct: FGDs & In-
Depth Interviews
Indirect: Projective
techniques
3. Secondary data
(Qualitative)
3. Case Study Analysis
1. Secondary data
(Quantitative)
2. Surveys
3. Observations
1. Experiments

3. Previous Session
Source: Malhotra and Dash (6e)
Conclusive/Confirmatory
Exploratory
Research Design
Descriptive Causal
1. Pilot Surveys
2. Qualitative Research
Direct: FGDs & In-
Depth Interviews
Indirect: Projective
techniques
3. Secondary data
(Qualitative)
3. Case Study Analysis
1. Secondary data
(Quantitative)
2. Surveys
3. Observations
1. Experiments

4. Necessary Conditions for Causality
1. Concomitant Variation
2. Time Order of Occurrence
3. Absence of Other Possible Causal Factors
X Y
Y
Advertising Experiment at
Restaurant Grades

5. Controlling Extraneous Variables
Randomisation:
o Random
assignment of
test units to
treatment
conditions
Matching:
o Comparing test units
on key background
variables before
assigning to treatment
conditions
Example: Matching
stores (on sales, size,
etc.) before varying
customers’ service
Statistical Control:
o Measuring
extraneous variables
& adjusting for their
effects through
statistical analysis
Example: ANCOVA
Design Control
o Use of
experiments
designed to
control specific
extraneous
variables

6. Qualtrics Experiment
https://iima.au1.qualtrics.com/jfe/form/SV_5gvZ2otZYh3bfng

7. Sample Questionnaire
1. Consent Form
2. Treatment Conditions/Control (Size of the gift menu: 3 gifts vs. 6 gifts)
3. Dependent Variable (Choice )
4. Manipulation Check (Perceived size of the gift menu)
5. Controls (Liking towards Gift)
6. Basic Information (Age, Gender)
7. Debriefing

8. Validity in Experimentation
Internal Validity:
o Whether manipulation of IVs actually caused
observed effects on DVs.
o Control of extraneous variables is a
necessary condition
External Validity:
o Whether cause-and-effect
relationships found in the experiment
can be generalized
o To what populations, settings, times,
IVs, and DVs can results be projected?
Lab
Experiments
Field
Experiments

9. Lab vs Field Experiments
Factor Laboratory Field
Environment Artificial Realistic
Control High Low
Demand artifacts High Low
Internal validity High Low
External validity Low High
Time Short Long
Number of units Small Large
Ease of implementation High Low
Cost Low High

10. Source: Malhotra and Dash (6e)
Conclusive/Confirmatory
Exploratory
Research Design
Descriptive Causal
1. Pilot Surveys
2. Qualitative Research
Direct: FGDs & In-
Depth Interviews
Indirect: Projective
techniques
3. Secondary data
(Qualitative)
3. Case Study Analysis
1. Secondary data
(Quantitative)
2. Surveys
3. Observations
1. Experiments

11. Limitations of Experiments
1. Experiments can be time consuming
2. Experiments are often expensive
3. Experiments can be difficult to administer
4. Competitors may deliberately contaminate results of field experiments

12. Source: Malhotra and Dash (6e)
Data Collection
Online Offline
Questionnaire Preparation
Softwares Online Data Collection
Platforms

13. Source: Malhotra and Dash (6e)
Data Collection
Online Offline
Step 5
Step 4
Step 3
Step 2
Step 1

14. Source: Malhotra and Dash (6e)
1. Descriptive
2. T-test
3. ANOVA
4. Regression Analysis

15. MBA Salaries

16. Context
Marie Dear: An aspiring MBA applicant, interested in the starting salaries of graduating
students.
Questions:
1. How much can students expect to make on graduation?
2. Are there any variables (e.g., gender, native language, quartile,
GMAT score, etc.) that affect the starting salaries of students?
3. Did students like the MBA program offered by the college?

17. Source: Malhotra and Dash (6e)
1. Descriptive
2. T-test
3. ANOVA
4. Regression Analysis

18. Thank You